In medical imaging, Picture Archiving and Communication Systems (PACS) are a combination of computers and/or networks dedicated to the storage, retrieval, presentation and distribution of images. While images may be stored in a variety of formats, the most common format for image storage within the PACS system is Digital Imaging and Communications in Medicine (DICOM). DICOM is a standard in which radiographic images and associated meta-data are communicated to the PACS system from imaging modalities for interaction by end-user medical personnel.
Medical personnel spend a significant amount of their time addressing administrative tasks. Such tasks include, for example, documenting patient interaction and treatment plans, preparing billing, reviewing lab results, recording observations, and preparing reports for health insurance. Time spent on performing such tasks diminish the time available for patients, and in some instances, lead to inaccurate and hastily compiled reports or records when personnel are faced with the need to see multiple patients.
In order to address time deficiency issues, the current trend in the medical field is to automate as many health care related processes as possible by leveraging various technologies, and thereby freeing up personnel to spend more time with patients rather than performing administrative tasks. Another objective in this arena is to ensure that when administrative tasks are performed, they are accomplished in an accurate and consistent manner. One approach to achieving this objective is to provide a standardized representation for healthcare related data, particularly within the various specialty areas, such as radiology, cardiology, etc.
Health care data is not easily reusable by disparate groups in the radiological field because it is stored with different methods and in different formats across a wide range of information technology. Various initiatives by groups and organizations across the globe, including the National Institutes of Health, Food and Drug Administration, and other medical bodies, have driven a set of standards for the consolidation of medical information into a common framework. One such standard is RadLex, which is a standard radiological lexicon proposed by the Radiological Society of North America, for uniform indexing and retrieval of radiology information. RadLex is a taxonomy having class hierarchies. RadLex functions essentially as a dictionary of terms and the notes relationships among the terms. RadLex has some crucial limitations. The most significant of these limitations is the inability to support radiological findings and the relationships between the findings and the characteristics of the findings. What is needed is an extension to RadLex—an extension that provides domain specific modeling, which can then be applied to or utilized by a wide variety of applications such as report tools, treatment analysis programs, tools for classification and verification of radiological information, and systems for improving radiological work flow. Such an extension would utilize an ontology that is domain specific to process and express radiological information.
Ontology is a data model for the modeling of concepts and the relationships between a set of concepts. Ontologies are utilized to illustrate the interaction between the set of concepts and corresponding relationships within a specific domain of interest. Thus, the concepts and the relationships between the concepts can be represented in readable text, wherein descriptions are provided to describe the concepts within a specific domain and the relationship axioms that constrain the interpretation of the domain specific concepts.
Numerous current products and research efforts offer tools that streamline data integration. These include centralized database projects such as the Functional Magnetic Resonance Imaging Data Center and the Protein Data Bank, distributed data collaboration networks such as the Biomedical Informatics Research Network, commercial tools for data organization, and systems for aggregating healthcare information such as Oracle Healthcare Transaction Base. In addition, tools have been developed to automatically validate data integrated into a common framework. Validation calls for techniques such as declarative interfaces between the ontology and the data source and Bayesian reasoning to incorporate prior expert knowledge about the reliability of each source.
While automated data integration and validation require fewer human resources, they necessitate that data have well-defined a priori structure and meaning.
There are a number of functionalities not provided by the systems described earlier. Accordingly, there is a need for a comprehensive system which is capable of enabling researchers to: 1) efficiently enter heterogeneous local data into the framework of the Unified Medical Language System (UMLS) based ontology; 2) make necessary extensions to the standardized ontology to accommodate local data; 3) validate the integrated data using expert rules and statistical models defined in the data classes of the standardized ontology; 4) efficiently upgrade data that fails validation; 5) leverage the integrated data for reporting functionality and predictions; and 6) express the integrated data unambiguously as prose text. This is particularly the case in the field of radiology, and even more specifically within the various domains therein such as mammography.
To overcome some of the deficiencies earlier described, some existing systems have attempted to minimize the amount of effort that may be required to report and express radiological findings. However, these systems suffer from a myriad of drawbacks. Essentially these solutions have a non-standard library or vocabulary, no error, terminology, or consistency checking, and no collaboration or tool that can be used by other application programs.
The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method for utilizing ontology that is based upon data obtained from unstructured and semi-structured knowledge sources to provide identification, validation and classification of radiological report concepts and to express those concepts as prose text.
The present invention addresses these needs as well as other needs.